Multi-focus image fusion using spatial frequency and discrete wavelet transform

Wei Tan; Hui-xin Zhou; Yue Yu; Juan Du; Hanlin Qin; Zhenhua Ma; Ruibo Zheng

doi:10.1117/12.2285561

24 October 2017 Multi-focus image fusion using spatial frequency and discrete wavelet transform

Wei Tan, Hui-xin Zhou, Yue Yu, Juan Du, Hanlin Qin, Zhenhua Ma, Ruibo Zheng

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104624K (2017) https://doi.org/10.1117/12.2285561
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

Pixel-level image fusion, which is widely used in remote sensing, medical imaging, surveillance and etc., directly combines the original information in the source images. As a pixel-level method, multi-focus image fusion is designed to combine the partially focused images into one fully fused single image, which is expected to be more informative for human or machine perception. To achieve this purpose, an algorithm using spatial frequency (SF) measure and discrete wavelet transform (DWT) for multi-focus image fusion is proposed. In this work, the source images are decomposed into low frequency components and high frequency components by using DWT. Then the spatial frequency of the low frequency components is calculated. The spatial frequency is used to judge the focused regions, followed by the morphological filter and median filter. The fused low frequency can be obtained. And the high frequency components are fused using traditional method. Finally, the fused image is obtained by doing inverse discrete wavelet transform. To do the comparison, the proposed algorithm is compared with several existing fusion algorithms in qualitative and quantitative ways. Experimental results demonstrate that our method can be competitive or even outperforms the methods in comparison.

Citation Download Citation

Wei Tan, Hui-xin Zhou, Yue Yu, Juan Du, Hanlin Qin, Zhenhua Ma, and Ruibo Zheng "Multi-focus image fusion using spatial frequency and discrete wavelet transform", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104624K (24 October 2017); https://doi.org/10.1117/12.2285561

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